In [1]:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

import os
import re
from imp import reload
import h5py
import sys
In [2]:
import deltascope as cranium
import deltascope.alignment as ut
In [3]:
at = ".\\data\\22hpf\\AT\\Prob"
zrf = ".\\data\\22hpf\\Gfap\\Prob"
In [4]:
outdir = ".\\data\\22hpf\\Output-02-14-2019"
In [5]:
os.mkdir(outdir)

---------------------------------------------------------------------------
FileExistsError                           Traceback (most recent call last)
<ipython-input-5-97d3b4674911> in <module>
----> 1 os.mkdir(outdir)

FileExistsError: [WinError 183] Cannot create a file when that file already exists: '.\\data\\22hpf\\Output-09-15'
In [6]:
Dat = {}
for f in os.listdir(at):
    if 'h5' in f:
        num  = re.findall(r'\d+',f.split('.')[0])[-1]
        Dat[num] = os.path.join(at,f)
In [7]:
Dzrf = {}
for f in os.listdir(zrf):
    if 'h5' in f:
        num  = re.findall(r'\d+',f.split('.')[0])[-1]
        Dzrf[num] = os.path.join(zrf,f)
In [8]:
Dbat = {}
In [9]:
Dbzrf = {}

Data Preprocessing

In [10]:
klist = Dat.keys()
In [11]:
param = {
    'gthresh':0.5,
    'scale':[1,1,1],
    'microns':[0.16,0.16,0.21],
    'mthresh':0.8,
    'radius':10,
    'comp_order':[0,2,1],
    'fit_dim':['x','z'],
    'deg':2
}
In [12]:
for k in klist:
    try:
        Dbat[k] = ut.preprocess(Dat[k],param)
        Dbzrf[k] = ut.preprocess(Dzrf[k],param,pca=Dbat[k].pcamed,mm=Dbat[k].mm,vertex=Dbat[k].vertex)
        print(k)
    except:
        print(k,'failed')
        e = sys.exc_info()
        print(e)

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

10

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

12

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

13

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

14

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

1

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

2

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

3

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

4

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

5

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

6

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

7

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

8

C:\Users\zfishlab\AppData\Local\Continuum\anaconda3\envs\test\lib\site-packages\skimage\util\dtype.py:141: UserWarning: Possible precision loss when converting from float32 to uint8
  .format(dtypeobj_in, dtypeobj_out))

9

Functions

In [13]:
def start(k):
    return(ut.start(k,Dbat,[Dbzrf],im=True))
def save_at(k,df):
    ut.save_at(k,df,outdir,'22hpf')
def save_both(k,dfa,dfb):
    ut.save_both(k,dfa,dfb,outdir,'22hpf')
In [14]:
def fit_model(axi,df,mm=None):
    if mm == None:
        mm = np.polyfit(df.x,df.z,2)
    p = np.poly1d(mm)
    xrange = np.arange(np.min(df.x),np.max(df.x))
    axi.plot(xrange,p(xrange),c='m')
    return(mm)
In [15]:
model = pd.DataFrame({'a':[],'b':[],'c':[]})
def save_model(k,mm,model):
    row = pd.Series({'a':mm[0],'b':mm[1],'c':mm[2]},name=k)
    model = model.append(row)
    return(model)
In [16]:
def pick_pts(x1,z1,vx,vz,x2,z2):
    pts = pd.DataFrame({'x':[x1,vx,x2],'z':[z1,vz,z2]})
    return(pts)
In [17]:
klist
Out[17]:
dict_keys(['10', '12', '13', '14', '1', '2', '3', '4', '5', '6', '7', '8', '9'])
In [18]:
Dbzrf.keys()
Out[18]:
dict_keys(['10', '12', '13', '14', '1', '2', '3', '4', '5', '6', '7', '8', '9'])

14

In [19]:
Dbat['14']
Out[19]:
<deltascope.brain at 0x244f0a9fa90>
In [20]:
k,df,Ldf,ax = start('14')
In [21]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'z')
In [22]:
pts.iloc[0].x = -40
pts.iloc[0].z = 26
pts.iloc[1].x = 45
pts.iloc[1].z = 22
ax[0,1].scatter(pts.x,pts.z,c='y')
pts
Out[22]:
x z
0 -40.0 26.0
1 45.0 22.0
In [23]:
df2,Ldf2,ax = ut.revise_pts(df,Ldf,'z',pts=pts)
In [24]:
mm = fit_model(ax[1,1],df2)
In [25]:
pts = pick_pts(-40,26,0,-5,45,22)
ax[1,1].scatter(pts.x,pts.z,c='m')
Out[25]:
<matplotlib.collections.PathCollection at 0x2451de638d0>
In [26]:
pts = pick_pts(-43,26,0,-5,45,22)
In [27]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2,pts=pts)
In [28]:
model = save_model(k,mm,model)
In [29]:
save_both(k,df3,Ldf3[0])

Write to .\data\22hpf\Output-09-15\AT_14_22hpf.psi complete
Write to .\data\22hpf\Output-09-15\ZRF_14_22hpf.psi complete

8

In [30]:
k,df,Ldf,ax = start('8')
In [31]:
mm = fit_model(ax[1,1],df)
In [32]:
model = save_model(k,mm,model)
In [33]:
save_both(k,df,Ldf[0])

Write to .\data\22hpf\Output-09-15\AT_8_22hpf.psi complete
Write to .\data\22hpf\Output-09-15\ZRF_8_22hpf.psi complete

6

In [34]:
k,df,Ldf,ax = start('6')
In [35]:
df1,Ldf1 = ut.zyswitch(df,Ldf)
ax = ut.make_graph([df1]+Ldf1)
In [36]:
df2,Ldf2 = ut.flip(df1,Ldf1)
ax = ut.make_graph([df2]+Ldf2)
In [37]:
mm = fit_model(ax[0,1],df2)
In [38]:
pts = pick_pts(-42,4,0,-15,40,5)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[38]:
<matplotlib.collections.PathCollection at 0x2451b5da400>
In [39]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2,pts=pts)
In [40]:
model = save_model(k,mm,model)
save_both(k,df3,Ldf3[0])

Write to .\data\22hpf\Output-09-15\AT_6_22hpf.psi complete
Write to .\data\22hpf\Output-09-15\ZRF_6_22hpf.psi complete

10

In [41]:
k,df,Ldf,ax = start('10')
In [42]:
pts = pick_pts(-32,18,0,-12,40,22)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[42]:
<matplotlib.collections.PathCollection at 0x2451ded0198>
In [43]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf,pts=pts)
In [44]:
model = save_model(k,mm,model)
save_both(k,df1,Ldf1[0])

Write to .\data\22hpf\Output-09-15\AT_10_22hpf.psi complete
Write to .\data\22hpf\Output-09-15\ZRF_10_22hpf.psi complete

7

In [45]:
k,df,Ldf,ax = start('7')
In [46]:
mm = fit_model(ax[0,1],df)
In [47]:
model = save_model(k,mm,model)
In [48]:
save_both(k,df,Ldf[0])

Write to .\data\22hpf\Output-09-15\AT_7_22hpf.psi complete
Write to .\data\22hpf\Output-09-15\ZRF_7_22hpf.psi complete

1

In [49]:
k,df,Ldf,ax = start('1')
In [50]:
mm = fit_model(ax[0,1],df)
In [51]:
pts = pick_pts(-48,15,0,-3,50,15)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[51]:
<matplotlib.collections.PathCollection at 0x2451ed3e748>
In [52]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf,pts=pts)
In [53]:
model = save_model(k,mm,model)
In [54]:
save_both(k,df1,Ldf1[0])

Write to .\data\22hpf\Output-09-15\AT_1_22hpf.psi complete
Write to .\data\22hpf\Output-09-15\ZRF_1_22hpf.psi complete

13

In [55]:
k,df,Ldf,ax = start('13')
In [56]:
df1,Ldf1,pts,ax = ut.check_pts(df,Ldf,'z')
In [57]:
pts.iloc[0].x = -45
pts.iloc[0].z = 17
pts.iloc[1].x = 42
pts.iloc[1].z = 14
ax[0,1].scatter(pts.x,pts.z,c='y')
pts
Out[57]:
x z
0 -45.0 17.0
1 42.0 14.0
In [58]:
df2,Ldf2,ax = ut.revise_pts(df,Ldf,'z',pts=pts)
In [59]:
df3,Ldf3,mm,ax = ut.ch_vertex(df2,Ldf2)
In [60]:
pts = pick_pts(-39,16,0,-6,47,16)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[60]:
<matplotlib.collections.PathCollection at 0x2451f730748>
In [61]:
df4,Ldf4,mm,ax = ut.ch_vertex(df3,Ldf3,pts=pts)
In [62]:
model = save_model(k,mm,model)
In [63]:
save_both(k,df4,Ldf4[0])

Write to .\data\22hpf\Output-09-15\AT_13_22hpf.psi complete
Write to .\data\22hpf\Output-09-15\ZRF_13_22hpf.psi complete

2

In [64]:
k,df,Ldf,ax = start('2')
In [65]:
mm = fit_model(ax[0,1],df)
In [66]:
model = save_model(k,mm,model)
save_both(k,df,Ldf[0])

Write to .\data\22hpf\Output-09-15\AT_2_22hpf.psi complete
Write to .\data\22hpf\Output-09-15\ZRF_2_22hpf.psi complete

12

In [67]:
k,df,Ldf,ax = start('12')
In [68]:
mm = fit_model(ax[0,1],df)
In [69]:
pts = pick_pts(-36,14,0,-11,43,16)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[69]:
<matplotlib.collections.PathCollection at 0x2451fb0a898>
In [70]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf,pts=pts)
In [71]:
model = save_model(k,mm,model)
save_both(k,df1,Ldf1[0])

Write to .\data\22hpf\Output-09-15\AT_12_22hpf.psi complete
Write to .\data\22hpf\Output-09-15\ZRF_12_22hpf.psi complete

4

In [72]:
k,df,Ldf,ax = start('4')
In [73]:
pts = pick_pts(-38,28,0,-13,40,30)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[73]:
<matplotlib.collections.PathCollection at 0x2451ee190f0>
In [74]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf,pts=pts)
In [75]:
model = save_model(k,mm,model)
save_both(k,df1,Ldf1[0])

Write to .\data\22hpf\Output-09-15\AT_4_22hpf.psi complete
Write to .\data\22hpf\Output-09-15\ZRF_4_22hpf.psi complete

9

In [76]:
k,df,Ldf,ax = start('9')
In [77]:
mm = fit_model(ax[0,1],df)
In [78]:
model = save_model(k,mm,model)
In [79]:
save_both(k,df,Ldf[0])

Write to .\data\22hpf\Output-09-15\AT_9_22hpf.psi complete
Write to .\data\22hpf\Output-09-15\ZRF_9_22hpf.psi complete

5

In [80]:
k,df,Ldf,ax = start('5')
In [81]:
mm = fit_model(ax[0,1],df)
In [82]:
model = save_model(k,mm,model)
save_both(k,df,Ldf[0])

Write to .\data\22hpf\Output-09-15\AT_5_22hpf.psi complete
Write to .\data\22hpf\Output-09-15\ZRF_5_22hpf.psi complete

3

In [83]:
k,df,Ldf,ax = start('3')
In [84]:
mm = fit_model(ax[0,1],df)
In [85]:
pts = pick_pts(-36,22,0,-5,38,20)
ax[0,1].scatter(pts.x,pts.z,c='m',s=50)
Out[85]:
<matplotlib.collections.PathCollection at 0x2457e9b7e48>
In [86]:
df1,Ldf1,mm,ax = ut.ch_vertex(df,Ldf,pts=pts)
In [87]:
model = save_model(k,mm,model)
save_both(k,df1,Ldf1[0])

Write to .\data\22hpf\Output-09-15\AT_3_22hpf.psi complete
Write to .\data\22hpf\Output-09-15\ZRF_3_22hpf.psi complete
In [88]:
model
Out[88]:
a b c
14 0.015011 7.442408e-17 -5.864644e-15
8 0.005242 -1.504477e-16 2.311078e-14
6 0.011614 -8.318195e-17 -3.975859e-15
10 0.024826 1.920145e-17 -1.037776e-14
7 0.005284 -1.208995e-16 4.298055e-15
1 0.007500 9.491636e-17 2.051180e-15
13 0.012002 1.511626e-16 3.079346e-15
2 0.007366 -7.175444e-17 7.848694e-16
12 0.016739 2.391703e-17 -1.238302e-14
4 0.027615 -6.906357e-17 -8.116310e-15
9 0.009719 -7.102912e-16 -1.980932e-15
5 0.007165 9.423889e-16 4.467195e-14
3 0.019026 4.753098e-17 1.243870e-14
In [89]:
model.to_csv(os.path.join(outdir,'model.csv'))
In [90]:
outdir
Out[90]:
'.\\data\\22hpf\\Output-09-15'